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020 _a9783031015854
_9978-3-031-01585-4
024 7 _a10.1007/978-3-031-01585-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aYang, Qiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982065
245 1 0 _aFederated Learning
_h[electronic resource] /
_cby Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXVII, 189 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Background -- Distributed Machine Learning -- Horizontal Federated Learning -- Vertical Federated Learning -- Federated Transfer Learning -- Incentive Mechanism Design for Federated Learning -- Federated Learning for Vision, Language, and Recommendation -- Federated Reinforcement Learning -- Selected Applications -- Summary and Outlook -- Bibliography -- Authors' Biographies.
520 _aHow is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_982066
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aLiu, Yang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982067
700 1 _aCheng, Yong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982068
700 1 _aKang, Yan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982069
700 1 _aChen, Tianjian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982070
700 1 _aYu, Han.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982071
710 2 _aSpringerLink (Online service)
_982072
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000300
776 0 8 _iPrinted edition:
_z9783031004575
776 0 8 _iPrinted edition:
_z9783031027130
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_982073
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01585-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c85292
_d85292